Department of Computer Science and Technology

Course pages 2019–20

Mobile Robot Systems

Principal lecturer: Dr Amanda Prorok
Taken by: Part II CST 75%

No. of lectures and practical classes: 16
Prerequisite courses: NST Mathematics, Artificial Intelligence, Algorithms.
Capacity: 40


This course teaches the foundations of autonomous mobile robots, covering topics such as perception, motion control, and planning. It also teaches algorithmic strategies that enable the coordination of multi-robot systems and robot swarms. The course will feature several practical sessions with hands-on robot programming. The students will undertake mini-projects, which will be formally evaluated through a report and presentation.


  • Robot motion and control. Kinematics, control models, trajectory tracking.
  • Control architectures. Sensor-actuator loops, reactive path planning.
  • Sensing. Sensors, perception.
  • Localization. Markov localization, environment modeling, SLAM.
  • Navigation. Planning, receding horizon control.
  • Multi-robot systems I. Centralization vs. decentralization, robot swarms.
  • Multi-robot systems II. Consensus algorithms, graph-theoretic methods.
  • Multi-robot systems III. Task assignment.
  • Multi-robot systems IV. Multi-robot path planning.


By the end of the course students should:

  • understand how to control a mobile robot;
  • understand how a robot perceives its environment;
  • understand how a robot plans actions (navigation paths);
  • know paradigms of coordination in systems of multiple robots;
  • know classical multi-robot problems and their solution methods;
  • Know how to use ROS (Robot Operating System,

Recommended reading

Siegwart, R., Nourbakhsh, I.R. & Scaramuzza, D. (2004). Autonomous mobile robots. MIT Press.
Thrun, S., Wolfram B. & Dieter F. (2005). Probabilistic robotics. MIT Press.
Mondada, F. & Mordechai B. (2018) Elements of Robotics. Springer
Siciliano, B. & Khatib, O. (2016) Springer handbook of robotics. Springer.
Mesbahi, M. & Egerstedt, M. (2010) Graph theoretic methods in multiagent networks. Princeton University Press.